Mapping and Characterizing Subtidal Oyster Reefs Using Acoustic Techniques, Underwater Videography and Quadrat Counts

Populations of the eastern oyster Crassostrea virginica have been in long-term decline in most areas. A major hindrance to effective oyster management has been lack of a methodology for accurately and economically obtaining data on their distribution and abundance patterns. Here, we describe early results from studies aimed at development of a mapping and monitoring protocol involving acoustic techniques, underwater videography, and destructive sampling (excavated quadrats). Two subtidal reefs in Great Bay, New Hampshire, were mapped with side-scan sonar and with videography by systematically imaging multiple sampling cells in a grid covering the same areas. A single deployment was made in each cell, and a 5-10-s recording was made of a 0.25-m2 area; the location of each image was determined using a differential global position system. A still image was produced for each of the cells and all (n = 40 or 44) were combined into a single photomontage overlaid onto a geo-referenced base map for each reef using Arc View geographic information system. Quadrat (0.25 m2 ) samples were excavated from 9 or 10 of the imaged areas on each reef, and all live oysters were counted and measured. Intercomparisons of the acoustic, video, and quadrat data suggest: (1) acoustic techniques and systematic videography can readily delimit the boundaries of oyster reefs; (2) systematic videography can yield quantitative data on shell densities and information on reef structure; and (3) some combination of acoustics, systematic videography, and destructive sampling can provide spatially detailed information on oyster reef characteristics

Low-Power Boards Enabling ML-Based Approaches to FDIR in Space-Based Applications

Modern satellite complexity is increasing, thus requiring bespoke and expensive on-board solutions to provide a Failure Detection, Isolation and Recovery (FDIR) function. Although FDIR is vital in ensuring the safety, autonomy, and availability of satellite systems in flight, there is a clear need in the space industry for a more adaptable, scalable, and cost-effective solution. This paper explores the current state of the art for Machine Learning error detection and prognostic algorithms utilized by both the space sector and the commercial sector. Although work has previously been done in the commercial sector on error detection and prognostics, most commercial applications are not nearly as limited by the power, mass, and radiation tolerance constraints as for operation in a space environment. Therefore, this paper also discusses several Commercial Off-The-Shelf (COTS) multi-core micro-processors, small-footprint boards that will be explored as possible testbeds for future integration into a satellite in-orbit demonstrator

Developing Machine Learning Models for Space Based Edge AI Platforms

On September 3rd 2020, one of the first small satellites equipped with Edge AI hardware was launched. The inclusion of a UB0100 board on PhiSat-1 enabled the use of deep neural networks to provide real-time image analysis on-board an Earth Observation satellite. The primary benefit of this was a 90% reduction in downlink data as the system only transmitted non-cloudy, and thus usable, data. PhiSat-1 and missions like it have started the revolution of satellite-based machine learning, leading ESA and other space agencies to further explore the in-situ deployment of machine-learning models. Other applications that can benefit from on-board space-based machine learning capabilities range from anomaly detection and prognostics to feature recognition and object detection. This paper focuses on the application of anomaly detection models on space-ready Edge AI hardware to detect and classify anomalous behaviour in telemetry data. The ability to accurately detect anomalies onboard satellite systems has the potential to both increase system lifetimes and reduce satellite operator workloads. The limitations of Edge AI boards and the space environment put restrictions on the models that can be used. Limited power and potential single event upsets constrain the complexity of the models that can be deployed. Therefore, this paper is targeted at models that will run efficiently within these constraints. We describe an experiment that evaluates the suitability of different anomaly detection approaches (multi-layer-perceptrons, auto-encoders, etc.) for space applications. These approaches are compared both in terms of their performance in the anomaly detection tasks and how well they run on “space ready” low-power hardware. We focus on the Intel Myriad chipset, the basis of the UB0100, which hosted the machine learning image analysis model on PhiSat-1. Our evaluations use both the MIMII machine audio dataset, a well-regarded anomaly detection dataset that is a good proxy for telemetry data, and a dataset generated using anonymized NASA mission telemetry data. The findings show how well basic models work when presented with anomalous satellite telemetry

Web-based sensor streaming wearable for respiratory monitoring applications.

This paper presents a system for remote monitoring of respiration of individuals that can detect respiration rate, mode of breathing and identify coughing events. It comprises a series of polymer fabric-sensors incorporated into a sports vest, a wearable data acquisition platform and a novel rich internet application (RIA) which together enable remote real-time monitoring of untethered wearable systems for respiratory rehabilitation. This system will, for the first time, allow therapists to monitor and guide the respiratory efforts of patients in real-time through a web browser. Changes in abdomen expansion and contraction associated with respiration are detected by the fabric sensors and transmitted wirelessly via a Bluetooth-based solution to a standard computer. The respiratory signals are visualized locally through the RIA and subsequently published to a sensor streaming cloud-based server. A web-based signal streaming protocol makes the signals available as real-time streams to authorized subscribers over standard browsers. We demonstrate real-time streaming of a six-sensor shirt rendered remotely at 40 samples/s per sensor with perceptually acceptable latency (<0.5s) over realistic network conditions

The inability of vaccinia virus A33R protein to form intermolecular disulfide-bonded homodimers does not affect the production of infectious extracellular virus

AbstractThe orthopoxvirus protein A33 forms a disulfide-bonded high molecular weight species that could be either a homodimer or a heteromultimer. The protein is a major target for neutralizing antibodies and the majority of antibodies raised against A33 only recognize the disulfide-bonded form. Here, we report that A33 is present as a disulfide-bonded homodimer during infection. Additionally, we examined the function of intermolecular disulfide bonding in A33 homodimerization during infection. We show that the cysteine at amino acid 62 is required for intermolecular disulfide bonding, but not dimerization as this mutant was still able to form homodimers. To investigate the role of disulfide-bonded homodimers during viral morphogenesis, recombinant viruses that express an A33R with cysteine 62 mutated to serine were generated. The recombinant viruses had growth characteristics similar to their parental viruses, indicating that intermolecular disulfide-bonded homodimerization of A33 is not required for its function

An Unobtrusive Method for Tracking Network Latency in Online Games

Online games are a very important class of distributed interactive applications. Their success is heavily dependant on the level of consistency that can be maintained between participants communicating in the virtual world. Achieving a high level of consistency usually involves the transmission of a large amount of network traffic. However, if the underlying network connecting participants is unable to process this traffic, then network latency will increase, which will in turn negatively impact on consistency. Many schemes exist which attempt to reduce network traffic, and thus reduce the effect of network latency on the interactive application. However, applications that employ these schemes tend to do so with little knowledge of the underlying network conditions, and assume a worst-case scenario of limited bandwidth. Such an assumption can actually cause these latency reduction schemes to perform sub-optimally, and ironically introduce more inconsistency than they reduce. Hence, it is important that online game applications become aware of network conditions, such as available bandwidth. Existing methods of estimating bandwidth operate by analysing trends in one-way latency, and require that extra data be transmitted between nodes in order to capture the latency trends. Such an approach does not suit online games, as the extra data requirements could increase network latency, and affect the ability of the application to scale to multiple participants. To deal with this issue, this paper proposes a method by which online games can unobtrusively track one-way network latency. This method requires no time-stamping information to be transmitted between participants and operates using data already being transmitted as part of the online game application, meaning that its impact on the network is minimal. NS2 simulations demonstrate that the trends collected by this method can be used to estimate bandwidth under certain conditions

Evaluating Performance of the Single Leg Squat Exercise with a Single Inertial Measurement Unit

The single leg squat (SLS) is an important component of lower limb rehabilitation and injury risk screening tools. This study sought to investigate whether a single lumbar-worn IMU is capable of discriminating between correct and incorrect performance of the SLS. Nineteen healthy volunteers (15 males, 4 females, age: 26.09±3.98 years, height: 1.75±0.14m, body mass: 75.2±14.2kg) were fitted with a single IMU on the lumbar spine and asked to perform 10 left leg SLS. These repetitions were recorded and labelled by a chartered physiotherapist. Features were extracted from the labelled sensor data. These features were used to train and evaluate a random-forests classifier. The system achieved an average of 92% accuracy, 78% sensitivity and 97% specificity. These results indicate that a single IMU has the potential to differentiate between a correctly and incorrectly completed SLS. This may allow such devices to be used by clinicians to help track rehabilitation of patients and screen for potential injury risks. Furthermore, the classifier described may be a useful input to an exercise biofeedback application

Characterisation of the Hoffmann Reflex using Mechanomyography

Mechanomyography (MMG) is a technique for recording mechanical activity in contracting muscle. The MMG signal is low frequency, typically 5-100Hz.This MMG ‘sound’ is produced by lateral oscillations of muscle fibres which occur at the resonant frequency of the muscle. The analysis of MMG signals has allowed examination of various aspects of muscle function such as neuromuscular fatigue, muscle fibre type distributions and neuromuscular disorders. To date, Electromyography (EMG) has been considered the primary non-invasive technique to record and interpret the physiological properties of contracting muscle. The Hoffmann reflex (H-reflex) is the equivalent of the monosynaptic stretch reflex, elicited by electrical stimulation. The aim of this investigation was to characterise the Hoffman reflex using an MMG system. The system is based on 2-axis MEMS (Micro Electro-Mechanical System) sensors placed on the soleus muscle

Characterisation of the Hoffmann Reflex using Mechanomyography

Mechanomyography (MMG) is a technique for recording mechanical activity in contracting muscle. The MMG signal is low frequency, typically 5-100Hz.This MMG ‘sound’ is produced by lateral oscillations of muscle fibres which occur at the resonant frequency of the muscle. The analysis of MMG signals has allowed examination of various aspects of muscle function such as neuromuscular fatigue, muscle fibre type distributions and neuromuscular disorders. To date, Electromyography (EMG) has been considered the primary non-invasive technique to record and interpret the physiological properties of contracting muscle. The Hoffmann reflex (H-reflex) is the equivalent of the monosynaptic stretch reflex, elicited by electrical stimulation. The aim of this investigation was to characterise the Hoffman reflex using an MMG system. The system is based on 2-axis MEMS (Micro Electro-Mechanical System) sensors placed on the soleus muscle

Wearable Kinematic And Physiological Biofeedback System For Movement Based Relaxation

Movement and mind control based arts such as Yoga and Tai Chi have been in existence for many centuries. In recent years much experimental evidence and research with a sound scientific basis has emerged to confirm that these arts / therapies have significant effects on the cardiovascular, respiratory and musculoskeletal systems. The main benefits of these ancient therapies include: 1. Promotion of mental and physiological relaxation 2. Enhanced body posture & musculoskeletal function 3. Improved cardiorespiratory function 4. Improved psychological well being & perceived quality of life